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Collaborative Filtering

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평가표

4

보존된 평가 기록

30

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/fajrianyunustelecomparis/stan-explainable

공식 코드

Explaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a Next Point of Interest Recommendation System

https://github.com/3140102441/CCCF

공식 코드

Compositional Coding for Collaborative Filtering

https://github.com/ACVAE/ACVAE-PyTorch

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Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation

https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model

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NAIS: Neural Attentive Item Similarity Model for Recommendation

https://github.com/Asa9aoTK/PNN-RecBole

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Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data

https://github.com/BlueGhostYi/BIGCF

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Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering

https://github.com/BoiseState/book-author-gender

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Exploring Author Gender in Book Rating and Recommendation

https://github.com/BoiseState/bookdata-tools

공식 코드

Exploring Author Gender in Book Rating and Recommendation

https://github.com/CRIPAC-DIG/DGCF

공식 코드

Dynamic Graph Collaborative Filtering

https://github.com/CRIPAC-DIG/LATTICE

공식 코드

Mining Latent Structures for Multimedia Recommendation

https://github.com/Coder-II/LFG

공식 코드

LFG: A Generative Network for Real-Time Recommendation

https://github.com/CognitiveComputationLab/ccobra

공식 코드

Uncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems

https://github.com/CrawlScript/Torch-MGDCF

공식 코드

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

https://github.com/DavyMorgan/ICDE20-PUP

공식 코드

Price-aware Recommendation with Graph Convolutional Networks

https://github.com/Diego999/HotelRec

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HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset

https://github.com/EpistasisLab/pennai

공식 코드

Evaluating recommender systems for AI-driven biomedical informatics

https://github.com/Evfro/fifty-shades

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Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

https://github.com/Evfro/polara

공식 코드

HybridSVD: When Collaborative Information is Not Enough

https://github.com/Evfro/recsys19_hybridsvd

공식 코드

HybridSVD: When Collaborative Information is Not Enough

https://github.com/FlorianWilhelm/lrann

공식 코드

On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks

https://github.com/GiuseppeSerra93/TLCM

공식 코드

An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviews

https://github.com/HKUST-KnowComp/FMG

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Side Information Fusion for Recommender Systems over Heterogeneous Information Network

https://github.com/JimLiu96/DeosciRec

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Deoscillated Graph Collaborative Filtering

https://github.com/JimLiu96/basConv

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BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network

https://github.com/JoeyDP/TEASER

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Modelling Users with Item Metadata for Explainable and Interactive Recommendation

https://github.com/Joinn99/CoRML

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Collaborative Residual Metric Learning

https://github.com/Joinn99/FPSR

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Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation

https://github.com/JoonyoungYi/sparsity-normalization

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Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

https://github.com/KNODIS-Research-Group/deep-variational-models-for-collaborative-filtering

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Deep Variational Models for Collaborative Filtering-based Recommender Systems

https://github.com/Kaike-Zhang/PamaCF

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Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation

https://github.com/KeithBurghardt/RecSim

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Emergent Instabilities in Algorithmic Feedback Loops

https://github.com/LTS4/modurec

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Modurec: Recommender Systems with Feature and Time Modulation

https://github.com/LehengTHU/Agent4Rec

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On Generative Agents in Recommendation

https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation

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A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research

https://github.com/MengtingWan/marketBias

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Addressing Marketing Bias in Product Recommendations

https://github.com/MunlikaRattaphun/Attention-on-Global-Local-Embedding-Spaces-in-Recommender-Systems

공식 코드

Attention on Global-Local Representation Spaces in Recommender Systems

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.